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- W2890655726 abstract "Widespread deployments of Network Function Virtualization (NFV) technology will replace many physical appliances in telecommunication networks with software executed on cloud platforms. Setting compute servers continuously to high-performance operating modes is a common NFV approach for achieving predictable operations. However, this has the effect that large amounts of energy are consumed even when little traffic needs to be forwarded. The Dynamic Voltage-Frequency Scaling (DVFS) technology available in Intel processors is a known option for adapting the power consumption to the workload, but it is not optimized for network traffic processing workloads. We developed a novel control method for DVFS, based observing the ongoing traffic and online predictions using machine learning. Our results show that we can save up to 27% compared to commodity DVFS, even when including the computational overhead of machine learning." @default.
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- W2890655726 date "2018-07-01" @default.
- W2890655726 modified "2023-09-27" @default.
- W2890655726 title "Improving Energy Efficiency in NFV Clouds with Machine Learning" @default.
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- W2890655726 doi "https://doi.org/10.1109/cloud.2018.00097" @default.
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